A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations

The present study aims to develop a systematic tool chain for automatically extracting accurate Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations for use in the design and operation of near-zero energy buildings, with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of ROMs when solved for different Boundary Conditions (BCs) in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures and mass flow rates. Results show that the absolute error can be kept under 0.5K for changes in temperature of up to ±15K and under 0.25K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has potential for applications in the built environment where its accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving energy efficiency in smart buildings.